Retailers face a difficult task when attempting to establish prices for the products that they offer. The task involves balancing the price of the products with consumer demand of the products. The task is made even more difficult if the retailers are confronted with many products that have to be priced.
Pricing of a specific product is rarely done in isolation. Instead, the process of establishing a price of a product involves consideration of the prices of related products (cross-prices). However, including the cross-prices of related products can quickly lead to a demand model with too many parameters to estimate—particularly with a demand model that attempts to price at a low level, such as at the Stock Keeping Unit (SKU) level. (The SKU is a unique number assigned to each style/size combination of a product.)
A current approach to limit the number of estimated cross-price parameters is described in the following publication: Bruce G. S. Hardie et al., “Attribute-based Market Share Models: Methodological Development and Managerial Applications,” University of Pennsylvania, Working Paper 98-009, pp. 1-48, 1998. The approach suggests developing measures of cross-price effects at the attribute level for each SKU. However, the cross-price variable approach disclosed therein exhibits significant disadvantages, such as not explaining demand well which adversely affects the performance of regression models that are attempting to predict demand.